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Optimizing Attention for Long-Sequence Processing
An engineering team is developing a language model for a task involving extremely long sequences, and they are facing out-of-memory errors due to the standard attention mechanism's growing key-value cache. They propose a modification where the query vector at any position i (q_i) only attends to the key-value pairs from the very first position (k_1, v_1) and its own current position (k_i, v_i). Analyze this proposed solution. Explain how it addresses the memory issue and identify a significant potential drawback regarding the model's ability to understand the sequence.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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An autoregressive model generates a sequence token by token. In a standard implementation, the query vector at position
i(q_i) computes attention over the key-value pairs from all preceding positions, from 1 toi. Consider a modified implementation where the queryq_iis restricted to attend only to the key-value pairs from the very first position (k_1,v_1) and its own current position (k_i,v_i). How does the computational cost of calculating the attention output for a single queryq_iscale as the sequence lengthigrows very large (e.g., from 100 to 10,000)?Trade-offs in Attention Mechanisms
Optimizing Attention for Long-Sequence Processing